Abstract 2182: Ancestry inference and population-specific disparities in a real-world clinical sequencing cohort
Authors: Kanika Arora, Thinh N. Tran, Yelena M. Kemel, Miika Mehine, Ying Liu, Shaleigh A. Smith, Subhiksha Nandakumar, Irina Ostrovnaya, Thomas C. Reynolds, Kenneth Offit, David Solit, Marc Ladanyi, Nikolaus Schultz, Ahmet Zehir, Carol L. Brown, Debyani Chakravarty, Zsofia K. Stadler, Chaitanya Bandlamudi, Michael F. Berger
Journal: Cancer Research (2022)

Abstract Accurate ancestry inference is crucial for identifying genetic determinants of cancer disparities among specific populations. While methods to infer genetic ancestry and admixture have historically relied upon genome-wide markers from broad-scale next-generation sequencing (NGS), the adaptation to targeted NGS panels presents an opportunity to prospectively incorporate ancestry inference as part of routine clinical diagnosis. Here we show that global ancestral contributions and admixture of African (AFR), European (EUR), East Asian (EAS), Native American (NAM) and South Asian (SAS) populations can be reliably inferred using markers from genomic regions covered by the FDA-authorized clinical NGS panel, MSK-IMPACT. We also show that individuals with Ashkenazi Jewish (ASJ) ancestry can be inferred with 97% accuracy using a set of ASJ ancestry-informative markers. We apply these methods to infer genetic ancestry for over 45,000 patients from the AACR GENIE v10.0-public cohort who were profiled using MSK-IMPACT. We observed 98% concordance between self-reported race and genetic ancestry for non-admixed individuals and were able to quantitatively infer ancestral contributions for individuals from recently admixed populations such as African American and Latin American. Of the discordant cases, manual review of clinical and family histories revealed the vast majority to represent clinical encoding errors where the inferred ancestry was confirmed correct. As self-reported race is not available for 17% of patients in the GENIE cohort, the ability to accurately infer genetic ancestry enables broader analysis of population-specific genomic and clinical features. We systematically evaluated the frequency of somatic and germline alterations in up to 468 genes for each cancer type and recapitulated known differences among ancestral populations. For example, we observed significantly higher frequency of EGFR somatic alterations in EAS (65%) and SAS (66%) compared to non-ASJ EUR (21%) with lung adenocarcinoma, a difference that remained significant even when limiting to never-smokers. Additionally, we found that the frequency of harboring at least one known BRCA founder mutation (BRCA1 68_69delAG, BRCA1 5266dupC, or BRCA2 5946delT) was 26x higher in genetically determined ASJ (5.1%) compared to non-ASJ (0.2%). Strikingly, while the overall rate of driver alterations in solid tumors was similar across different populations, we found that the proportion of patients with clinically actionable somatic alterations (OncoKB Level 1, 2, 3A, or 3B) was lowest in AFR (47%) patients compared to EUR, EAS and SAS (50% each). While this result is partially explained by different cancer type and subtype distributions in different populations in this real-world cohort, it highlights the urgency for greater equity in drug development programs to target alterations represented across all diverse populations. Citation Format: Kanika Arora, Thinh N. Tran, Yelena M. Kemel, Miika Mehine, Ying Liu, Shaleigh A. Smith, Subhiksha Nandakumar, Irina Ostrovnaya, Thomas C. Reynolds, Kenneth Offit, David Solit, Marc Ladanyi, Nikolaus Schultz, Ahmet Zehir, Carol L. Brown, Debyani Chakravarty, Zsofia K. Stadler, Chaitanya Bandlamudi, Michael F. Berger. Ancestry inference and population-specific disparities in a real-world clinical sequencing cohort [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2182.